Hybrid Comparative Modeling of ANN and SVM for Accurate Machining Performance Prediction of AlSiC MMC

Abstract This study presents a hybrid machine learning model that combines Artificial Neural Networks (ANN) and Support Vector Machines (SVM) to model critical machining performance parameters such as surface roughness (Ra), material cutting force (Fc) as well as material removal rate (MRR) in the machining of aluminum silicon carbide (AlSiC) metal matrix composite by turning. A synthetic data set consisting of literature sources validated on a model-to-case basis was employed to train both individual models and the architecture itself and its performance was assessed through the statistical metrics (R2, RMSE, MAE) and the visual inspection of the data (parity plots, residual histograms). ANN was proven to be more accurate in modeling nonlinear surface roughness behavior, and SVM was more accurate in estimating MRR since it had the capacity to have a smooth generalization. The inverse-RMSE weighted hybrid ensemble was always better at the task than the two standalone models and provided lower RMSE values with the minima of 0.055 µm on Ra and 2.361 N on Fc. These findings validate the soundness and applicability of the hybrid model as a digital twin to CAM-based process leads the way with tool-life management and efficiency maximization. The applications in adaptive precision machining such as real-time validation and incorporation of the other sensor variables will be done in the future.